Exploratory Run Results
1 Overview
Exploratory runs were carried out to see how selection schemes perform at a fundamental level. The results of these run are divded by diagnotic problem, and further by selection scheme.
2 Diagnostics
For this diagnostic there were 100 objectives to optimize, all with the target of 100. The population was size 512, and there was a point mutation probability of .7% at each objective.
2.1 Exploitation
2.1.1 Mu Lambda Selection
2.1.1.1 Population Aggregate Fitness Average
2.1.1.2 Population Aggregate Fitness Average
2.1.1.3 Population Unique Optimal Count Average
2.1.1.4 Common Solution Count
2.1.1.5 Common Solution Aggregate Performance
2.1.1.6 Common Solution Optimization Count
2.1.1.7 Elite Solution Aggregate Performance
2.1.1.8 Elite Solution Optimization Count
2.1.1.9 Optimal Solution Aggregate Performance
2.1.1.10 Optimal Solution Optimization Count
2.1.1.11 Loss in Diversity
2.1.1.12 Selection Pressure
2.1.2 Tournament Selection
2.1.2.1 Population Aggregate Fitness Average
2.1.2.2 Population Aggregate Fitness Average
2.1.2.3 Population Unique Optimal Count Average
2.1.2.4 Common Solution Count
2.1.2.5 Common Solution Aggregate Performance
2.1.2.6 Common Solution Optimization Count
2.1.2.7 Elite Solution Aggregate Performance
2.1.2.8 Elite Solution Optimization Count
2.1.2.9 Optimal Solution Aggregate Performance
2.1.2.10 Optimal Solution Optimization Count
2.1.2.11 Loss in Diversity
2.1.2.12 Selection Pressure
2.1.3 Lexicase Selection
2.1.3.1 Population Aggregate Fitness Average
2.1.3.2 Population Aggregate Fitness Average
2.1.3.3 Population Unique Optimal Count Average
2.1.3.4 Common Solution Count
2.1.3.5 Common Solution Aggregate Performance
2.1.3.6 Common Solution Optimization Count
2.1.3.7 Elite Solution Aggregate Performance
2.1.3.8 Elite Solution Optimization Count
2.1.3.9 Optimal Solution Aggregate Performance
2.1.3.10 Optimal Solution Optimization Count
2.1.3.11 Loss in Diversity
2.1.3.12 Selection Pressure
2.2 Structured Exploitation
2.2.1 Mu Lambda Selection
2.2.1.1 Population Aggreagate Fitness
2.2.1.2 Population Optimal Count Average
2.2.1.3 Population Unique Optimal Count Average
2.2.1.4 Common Solution Count
2.2.1.5 Common Solution Aggregate Performance
2.2.1.6 Common Solution Optimization Count
2.2.1.7 Elite Solution Aggregate Performance
2.2.1.8 Elite Solution Optimization Count
2.2.1.9 Optimal Solution Aggregate Performance
2.2.1.10 Optimal Solution Optimization Count
2.2.1.11 Loss in Diversity
2.2.1.12 Selection Pressure
2.2.2 Tournament Selection
2.2.2.1 Population Aggreagate Fitness
2.2.2.2 Population Optimal Count Average
2.2.2.3 Population Unique Optimal Count Average
2.2.2.4 Common Solution Count
2.2.2.5 Common Solution Aggregate Performance
2.2.2.6 Common Solution Optimization Count
2.2.2.7 Elite Solution Aggregate Performance
2.2.2.8 Elite Solution Optimization Count
2.2.2.9 Optimal Solution Aggregate Performance
2.2.2.10 Optimal Solution Optimization Count
2.2.2.11 Loss in Diversity
2.2.2.12 Selection Pressure
2.2.3 Lexicase Selection
2.2.3.1 Population Aggreagate Fitness
2.2.3.2 Population Optimal Count Average
2.2.3.3 Population Unique Optimal Count Average
2.2.3.4 Common Solution Count
2.2.3.5 Common Solution Aggregate Performance
2.2.3.6 Common Solution Optimization Count
2.2.3.7 Elite Solution Aggregate Performance
2.2.3.8 Elite Solution Optimization Count
2.2.3.9 Optimal Solution Aggregate Performance
2.2.3.10 Optimal Solution Optimization Count
2.2.3.11 Loss in Diversity
2.2.3.12 Selection Pressure
2.3 Contradictory Ecology
2.3.1 Mu Lambda Selection
2.3.1.1 Population Optimal Count Average
2.3.1.2 Population Unique Optimal Count Average
2.3.1.3 Common Solution Count
2.3.1.4 Common Solution Aggregate Performance
2.3.1.5 Common Solution Optimization Count
2.3.1.6 Elite Solution Aggregate Performance
2.3.1.7 Elite Solution Optimization Count
2.3.1.8 Optimal Solution Aggregate Performance
2.3.1.9 Optimal Solution Aggregate Performance
2.3.1.10 Optimal Solution Optimization Count
2.3.1.11 Loss in Diversity
2.3.1.12 Selection Pressure
2.3.2 Tournament Selection
2.3.2.1 Population Aggreagate Fitness
2.3.2.2 Population Optimal Count Average
2.3.2.3 Population Unique Optimal Count Average
2.3.2.4 Common Solution Count
2.3.2.5 Common Solution Aggregate Performance
2.3.2.6 Common Solution Optimization Count
2.3.2.7 Elite Solution Aggregate Performance
2.3.2.8 Elite Solution Optimization Count
2.3.2.9 Optimal Solution Aggregate Performance
2.3.2.10 Optimal Solution Optimization Count
2.3.2.11 Loss in Diversity
2.3.2.12 Selection Pressure
2.3.3 Lexicase Selection
2.3.3.1 Population Aggreagate Fitness
2.3.3.2 Population Optimal Count Average
2.3.3.3 Population Unique Optimal Count Average
2.3.3.4 Common Solution Count
2.3.3.5 Common Solution Aggregate Performance
2.3.3.6 Common Solution Optimization Count
2.3.3.7 Elite Solution Aggregate Performance
2.3.3.8 Elite Solution Optimization Count
2.3.3.9 Optimal Solution Aggregate Performance
2.3.3.10 Optimal Solution Optimization Count
2.3.3.11 Loss in Diversity
2.3.3.12 Selection Pressure
2.4 Exploration
2.4.1 Mu Lambda Selection
2.4.1.1 Population Aggreagate Fitness
2.4.1.2 Population Optimal Count Average
2.4.1.3 Population Unique Optimal Count Average
2.4.1.4 Common Solution Count
2.4.1.5 Common Solution Aggregate Performance
2.4.1.6 Common Solution Optimization Count
2.4.1.7 Elite Solution Aggregate Performance
2.4.1.8 Elite Solution Optimization Count
2.4.1.9 Optimal Solution Aggregate Performance
2.4.1.10 Optimal Solution Optimization Count
2.4.1.11 Loss in Diversity
2.4.1.12 Selection Pressure
2.4.2 Tournament Selection
2.4.2.1 Population Aggreagate Fitness
2.4.2.2 Population Optimal Count Average
2.4.2.3 Population Unique Optimal Count Average
2.4.2.4 Common Solution Count
2.4.2.5 Common Solution Aggregate Performance
2.4.2.6 Common Solution Optimization Count
2.4.2.7 Elite Solution Aggregate Performance
2.4.2.8 Elite Solution Optimization Count
2.4.2.9 Optimal Solution Aggregate Performance
2.4.2.10 Optimal Solution Optimization Count
2.4.2.11 Loss in Diversity
2.4.2.12 Selection Pressure
2.4.3 Lexicase Selection
2.4.3.1 Population Aggreagate Fitness
2.4.3.2 Population Optimal Count Average
2.4.3.3 Population Unique Optimal Count Average
2.4.3.4 Common Solution Count
2.4.3.5 Common Solution Aggregate Performance
2.4.3.6 Common Solution Optimization Count
2.4.3.7 Elite Solution Aggregate Performance
2.4.3.8 Elite Solution Optimization Count
2.4.3.9 Optimal Solution Aggregate Performance
2.4.3.10 Optimal Solution Optimization Count
2.4.3.11 Loss in Diversity
2.4.3.12 Selection Pressure
